The Statistical Core serves two functions that are essential for the success of the Einstein Aging Study (EAS). First, the Statistical Core maximizes data quality by implementing a database system that integrates and manages the data collected from the Administrative, Clinical, Neuropathology and Neuroimaging Cores, and from each of the four projects. The Statistical Core assumes responsibility for quality control procedures and for merging data across Projects and Cores. Second, the Statistical Core provides collaborative and consultative support to Project investigators on matters of study design, data analyses and interpretation of results. The Statistical Core is responsible for developing, implementing and interpreting statistical methods appropriate to specific research questions and hypotheses, and it collaborates regulariy with Project investigators on scientific manuscripts. Specific Aims of the Statistical Core are: Aim 1. To implement and oversee data procedures to facilitate the seamless exchange of data and ideas among Cores and Projects, and to facilitate data transfer for collaborations with investigators outside the EAS. Aim 2. To provide a general analytic framework for hypothesis testing, model building and integration of results and analyses across measurement constructs (e.g., exposures, mechanisms, outcomes), and to collaborate with investigators regarding the framing and testing of hypotheses and to provide expertise in the design and conduct of analyses. Aim 3. To develop new statistical methodology and to apply existing methodology in innovative ways to help to fulfill the other aims of this Core and the Projects and to further aging research in general. RELEVANCE (See instructions): The Statistical Core's contribution to the Program Project is essential and significant because it is needed (1) To ensure a high quality of data;(2) To ensure proper analysis of data for hypothesis testing and hypothesis generation;and (3) To ensure that the data collected in each project and core are optimally utilized for both project specific and cross-project analyses.